GEO series

Single-cell spatial transcriptomic profiling defines a pathogenic inflammatory niche in chronic active multiple sclerosis lesions - snRNA-seq

GSE301908 Homo sapiens (human) Single-nucleus RNA-seq 17 samples Submitted 2025/10/29 Platform GPL24676

At a glance

Single-nucleus RNA-seq · human · 17 samples

What it is: Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue).
Organism: Homo sapiens (human)
Samples: 17 samples: 17 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
Data you can get: A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.

Summary (written by the submitters)

Compartmentalized inflammation is considered a critical factor in driving the progression of multiple sclerosis (MS). Yet, the mechanisms sustaining its persistence remain poorly understood. A hallmark of this persistent and slowly evolving inflammatory process are chronic active MS lesions. In this study, we created a high-resolution, single-cell molecular and spatial atlas of chronic inflammation in MS.

Read the full summary ↓

To accomplish this, we combined single-nucleus RNA sequencing (snRNA-seq) with single-cell spatial transcriptomics using multiplexed error-robust fluorescence in situ hybridization (MERFISH) to examine MS lesions, specifically focusing on those exhibiting chronic active immune pathology characterized by lymphocyte presence. Our integrative profiling uncovered the molecular landscape of glial and immune cells, their disease-associated states, and the surrounding microenvironments. Within the lesion rim, we identified CD8+ T cell niches with inflamed, foamy microglia, characterized by an interferon response and dysfunctional lipid metabolism. To investigate the function of these microglia, we deleted ATP-binding cassette transporters A1 and G1 (ABCA1/G1) in microglia of mice and induced experimental autoimmune encephalomyelitis (EAE). Our findings revealed that inhibiting cholesterol efflux increased the formation of lipid-storing phagocytes, which actively drove inflammatory processes in EAE. Moreover, we found that pharmacologically targeting sterol metabolism presents a promising therapeutic strategy for mitigating inflammation in EAE. Thus, we have created a high-resolution map of immune niches in chronic active MS lesions, leading to the discovery of lipid-associated microglia as key drivers of neuroinflammation.

This dataset

Open GSE301908 in finder → Find more human RNA-seq datasets →

Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE301908_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1287730 and SRA study SRP598916. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

17 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 17 sample titles: 17 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

GSM9092141 MS1_snRNA-seq
GSM9092142 MS2_snRNA-seq
GSM9092143 MS3_snRNA-seq
GSM9092144 MS4_snRNA-seq
GSM9092145 MS5_snRNA-seq
GSM9092146 MS6_snRNA-seq
GSM9092147 MS7_snRNA-seq
GSM9092148 MS8_snRNA-seq
GSM9092149 MS9_snRNA-seq
GSM9092150 MS10_snRNA-seq
GSM9092151 MS11_snRNA-seq
GSM9092152 MS12_snRNA-seq
GSM9092153 MS13_snRNA-seq
GSM9092154 MS14_snRNA-seq
GSM9092155 Ctrl1_snRNA-seq
GSM9092156 Ctrl2_snRNA-seq
GSM9092157 Ctrl3_snRNA-seq

Datasets on a similar topic

GSE279180 Cell type mapping reveals tissue niches and interactions in subcortical multiple sclerosis lesions [snRNA-seq] 16 samples shares sclerosis, snrna-seq, multiple
GSE305971 Integrative single-cell analysis reveals malignant archetypes and proliferative hierarchies in multiple myeloma 2105 samples shares single-cell, multiple
GSE310058 Single-nucleus transcriptomic profiling of human lung tissue in chronic obstructive pulmonary disease (COPD) [snRNA-seq] 149 samples shares snrna-seq, chronic
GSE330029 Temporal changes in metabolism guide oligodendrocyte precursor cell dynamics in aging and multiple sclerosis [BulkRNAseq] 108 samples shares sclerosis, multiple
GSE324131 A single-cell and spatial atlas of prostate cancer reveals the combinatorial nature of gene modules underlying lineage plasticity and metastasis 38 samples shares single-cell, spatial
GSE278688 Integrated Single-Cell and Spatial Transcriptomics Uncover Distinct Cellular Subtypes Involved in Neural Invasion in PDAC [scRNA-Seq] 29 samples shares single-cell, spatial
GSE176078 A single-cell and spatially resolved atlas of human breast cancers 26 samples shares single-cell, spatial
GSE271107 Single-cell RNA sequencing of bone marrow aspirate samples from multiple myeloma and its precursor conditions 19 samples shares single-cell, multiple

Search all human RNA-seq datasets in GEO →

Share this dataset

Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.